小波神经网络MATLAB程序

               

clear all %initiate of data P=3 %numberof sample m=1%number of input node n=10%number of hidden node N=1%number of ouptut node % %a(n) b(n) scale and shifting parameter matrix %x(P,m) input matrix of P sample %net(P,n) ouput of hidden node %y(P,N) output of network %d(P,N) ideal output of network % phi(P,n) ouput of hidden node wavelet funciton %W(N,n)weight value between ouput and hidden %WW(n,m) weight value between hidden and input node x=[4;5;6] d=[1.3;3.6;6.7] W=rand(N,n) WW=rand(n,m) a=ones(1,n) for j=1:n b(j)=j*P/n; end %%%%%%%%%%%%%%%%%% %EW(N,n) gradient of W %EWW(n,m) gradient of WW %Ea(n) gradient of a %Eb(n) gradient of b %%%%%%%%%%%%%%] epoch=1; epo=100; error=0.05; err=0.01; delta =1; lin=0.5; while (error>=err & epoch<=epo) u=0;%u is the middle variant %caculation of net input for p=1:P for j=1:n u=0; for k=1:m u=u+WW(j,k)*x(p,k); end net(p,j)=u; end end %calculation of morlet 0r mexican wavelet output for p=1:P for j=1:n u=net(p,j); u=(u-b(j))/a(j); phi(p,j)=cos(1.75*u)*exp(-u*u/2); %morlet wavelet %phi(p,j)=(1-u^2)*exp(-u*u/2); %mexican hat wavelet end end %calculation of output of network for p=1:P for i=1:N u=0; for j=1:n u=u+W(i,j)*phi(p,j); end y(p,i)=delta*abs(u); end end %calculation of error of output u=0; for p=1:P for i=1:N u=u+abs(d(p,i)*log(y(p,i))+(1-d(p,i)*log(1-y(p,i)))); %u=u+(d(p,i)-y(p,i))^2; end end %u=u/2 error=u; %calculate of gradient of network for i=1:N for j=1:n u=0; for p=1:P u=u+(d(p,i)-y(p,i))*phi(p,j); end EW(i,j)=u; %EW(i,j)=-u;%the resule would be wrong end end for j=1:n for k=1:m u=0 for p=1:P for i=1:N u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*x(p,k)/a(j) ; end end EWW(j,k)=u; %EWW(j,k)=u the result would be wrong end end for j=1:n u=0 for p=1:P for i=1:N u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)/a(j) ; end end Eb(j)=u; end for j=1:n u=0 for p=1:P for i=1:N u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*((net(p,j)-b(j))/b(j))/a(j) ; end end Ea(j)=u; end %adjust of weight value WW=WW-lin*EWW; W=W-lin*EW; a=a-lin*Ea; b=b-lin*Eb; %number of epoch increase by 1 epoch=epoch+1; end

            clear all %initiate of data P=3 %numberof sample m=1%number of input node n=10%number of hidden node N=1%number of ouptut node % %a(n) b(n) scale and shifting parameter matrix %x(P,m) input matrix of P sample %net(P,n) ouput of hidden node %y(P,N) output of network %d(P,N) ideal output of network % phi(P,n) ouput of hidden node wavelet funciton %W(N,n)weight value between ouput and hidden %WW(n,m) weight value between hidden and input node x=[4;5;6] d=[1.3;3.6;6.7] W=rand(N,n) WW=rand(n,m) a=ones(1,n) for j=1:n b(j)=j*P/n; end %%%%%%%%%%%%%%%%%% %EW(N,n) gradient of W %EWW(n,m) gradient of WW %Ea(n) gradient of a %Eb(n) gradient of b %%%%%%%%%%%%%%] epoch=1; epo=100; error=0.05; err=0.01; delta =1; lin=0.5; while (error>=err & epoch<=epo) u=0;%u is the middle variant %caculation of net input for p=1:P for j=1:n u=0; for k=1:m u=u+WW(j,k)*x(p,k); end net(p,j)=u; end end %calculation of morlet 0r mexican wavelet output for p=1:P for j=1:n u=net(p,j); u=(u-b(j))/a(j); phi(p,j)=cos(1.75*u)*exp(-u*u/2); %morlet wavelet %phi(p,j)=(1-u^2)*exp(-u*u/2); %mexican hat wavelet end end %calculation of output of network for p=1:P for i=1:N u=0; for j=1:n u=u+W(i,j)*phi(p,j); end y(p,i)=delta*abs(u); end end %calculation of error of output u=0; for p=1:P for i=1:N u=u+abs(d(p,i)*log(y(p,i))+(1-d(p,i)*log(1-y(p,i)))); %u=u+(d(p,i)-y(p,i))^2; end end %u=u/2 error=u; %calculate of gradient of network for i=1:N for j=1:n u=0; for p=1:P u=u+(d(p,i)-y(p,i))*phi(p,j); end EW(i,j)=u; %EW(i,j)=-u;%the resule would be wrong end end for j=1:n for k=1:m u=0 for p=1:P for i=1:N u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*x(p,k)/a(j) ; end end EWW(j,k)=u; %EWW(j,k)=u the result would be wrong end end for j=1:n u=0 for p=1:P for i=1:N u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)/a(j) ; end end Eb(j)=u; end for j=1:n u=0 for p=1:P for i=1:N u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*((net(p,j)-b(j))/b(j))/a(j) ; end end Ea(j)=u; end %adjust of weight value WW=WW-lin*EWW; W=W-lin*EW; a=a-lin*Ea; b=b-lin*Eb; %number of epoch increase by 1 epoch=epoch+1; end